Improving correspondence matching using label consistency constraints

In this paper we demonstrate how to embed label consistency constraints into point correspondence matching. We make two contributions. First, we show how the point proximity matrix can be incorporated into the support function for probabilistic relaxation. Second we show how the label probabilities delivered by relaxation labelling can be used to gate the kernel matrix for articulated point pattern matching. The method is evaluated on synthetic and real-world data, where the label compatibility process is demonstrated to improve the correspondence process.